code: def _augment_feature(
      self, dataset, features, feature_dims, as_label=False
  ):
      # The feature dims actually used might differ from user specified
      # feature_dims.
      # This could be due to balanced binning on integers where
      # different bin edges have the same value
      if as_label:
          repr_method = (
              "balanced"
              if "classification" in cfg.dataset.task_type
              else "original"
          )
      else:
          repr_method = cfg.dataset.augment_feature_repr
      actual_feat_dims = []
      for key, dim in zip(features, feature_dims):
          feat_fun = self.feature_dict[key]
          key = key + "_label" if as_label else key
          if key not in dataset[0]:
              # compute (raw) features
              dataset.apply_transform(
                  feat_fun,
                  update_graph=False,
                  update_tensor=False,
                  as_label=as_label,
                  feature_dim=dim,
              )
              # feat = dataset[0][key]
              if repr_method == "original":
                  # use the original feature as is
                  # this ignores the specified config feature_dims
                  dataset.apply_transform(
                      FeatureAugment._orig_features,
                      update_graph=True,
                      update_tensor=False,
                      key=key,
                  )
              elif repr_method == "position":
                  # positional encoding similar to that of transformer
                  scale = (
                      dim
                      / 2
                      / FeatureAugment._get_max_value(dataset, key)
                  )
                  dataset.apply_transform(
                      FeatureAugment._position_features,
                      update_graph=True,
                      update_tensor=False,
                      key=key,
                      feature_dim=dim,
                      scale=scale,
                  )
              else:
                  # Bin edges for one-hot (repr_method = balanced, bounded,
                  # equal_width)
                  # use all features in dataset for computing bin edges
                  bin_edges = self._get_bin_edges(
                      dataset, key, dim, repr_method
                  )
                  # apply binning
                  dataset.apply_transform(
                      FeatureAugment._bin_features,
                      update_graph=True,
                      update_tensor=False,
                      key=key,
                      bin_edges=bin_edges,
                      feature_dim=len(bin_edges),
                      as_label=as_label,
                  )
          actual_feat_dims.append(
              dataset[0].get_num_dims(key, as_label=as_label)
          )
      return actual_feat_dims
  
imports: import logging
  import networkx
  import numpy
  import torch
  import torch.nn
  from deepsnap.graph import Graph
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.contrib.transform.identity import compute_identity
label_desc: Use image node_id 9 for calling the FeatureAugment obj's underlying member method code with example usage: obj._augment_feature(dataset, features, feature_dims, as_label) and returns: actual_feat_dims